Secure AI Enclaves: VPC & On‑Prem Deployment 30-Day Plan

Three questions auditors and Legal will ask (and how enclaves answer them)

“VPC vs on‑prem” is usually framed as a hosting argument. For CISO/GC/Audit, it’s a control argument: network containment, identity boundaries, and evidentiary logging.

A secure enclave is the combination of: private connectivity to data sources, an LLM gateway that enforces policy, controlled egress, and durable logs you can hand to auditors without a scramble.

• Where did the data travel? (network paths, egress control, regions)

• Who could access it? (RBAC, service identities, break-glass)

• What evidence exists? (prompt/response logs, approvals, retention, tamper resistance)

When VPC is enough—and when it isn’t

In practice, most enterprises land on two enclave tiers: a “private cloud enclave” for 80% of sensitive use cases and an “on‑prem enclave” for the highest-regulation or sovereignty-bound workloads. The governance win is standardizing controls across both tiers so teams aren’t reinventing security per copilot.

• VPC is often sufficient for: internal-only data, strong DLP, and clear regional requirements.

• On‑prem is justified when: regulatory constraints demand physical control, air-gapped segments exist, or ultra-low latency/sovereignty requirements override cloud economics.

• Hybrid is common: inference in VPC, retrieval to on‑prem data via private connectivity, with strict egress rules and audited connectors.

Core components (what you need to be true)

This is where “governance becomes a growth enabler.” When the enclave is a paved road, product teams can ship support copilots, document extraction, or an AI Knowledge Assistant without negotiating security from scratch every time.

DeepSpeed AI deployments typically run on AWS, Azure, or GCP in your accounts (VPC/VNet), integrate with identity providers (Okta/Azure AD), and connect to systems like Salesforce, ServiceNow, Zendesk, Slack, and Teams. For analytics-heavy workflows, we commonly connect to Snowflake, BigQuery, and Databricks with regional controls and audit-friendly service identities.

• LLM gateway in your VPC: enforces RBAC, redaction, allowlists, rate limits, and logs prompts/responses.

• Private retrieval: connectors to Snowflake/BigQuery/Databricks/SharePoint/Confluence via private endpoints; vector DB in-region.

• Egress deny-by-default: outbound restricted to approved model endpoints or on‑prem model servers; exceptions require approval.

• Observability: request tracing, model latency, retrieval hit rate, refusal rate, and “unsafe output” detections.

• Evidence pipeline: immutable log storage, retention policies, and automated access reviews.

How sensitive data stays inside the boundary

Most “AI data leaks” are really “unbounded retrieval + unlogged prompts + permissive egress.” Enclaves solve this by making retrieval explicit, egress narrow, and logs complete.

For document-heavy sensitive workloads (contracts, claims, KYC packets), our Document and Contract Intelligence pattern runs extraction + classification inside the enclave, with outputs stored back into your systems and evidence logged for each run.

• Data minimization: retrieve only the fields needed for the task; mask identifiers at the gateway.

• Scoped embeddings: per-tenant or per-business-unit indexes; separate KMS keys; region-locked vector stores.

• No training on client data: models are never fine-tuned on your prompts unless you explicitly choose it—and even then, only in your controlled environment.

• Human-in-the-loop gates for high-risk actions: e.g., sending customer communications or approving contract redlines.

What changes on‑prem

On‑prem is not a shortcut around governance—it’s a higher bar for operational maturity. The biggest failure mode we see is “we went on‑prem for security” but didn’t implement consistent gateway policy, logging, and approvals, which makes audit evidence weaker, not stronger.

• Compute and model serving run on your hardware (often Kubernetes); patching and capacity planning are yours.

• Network segmentation is usually stronger but requires disciplined firewall and DNS policies.

• Model update cadence becomes a governance process (change approvals, regression tests, rollback plans).

What should stay consistent across VPC and on‑prem

If you can’t answer “show me every time this workflow touched Restricted data” across both environments, you will keep re-litigating risk with every new copilot request. Consistency is what buys speed.

• One policy model for RBAC, data classes, regions, and retention.

• One log schema for prompts, retrieval sources, and outputs (so Audit can test uniformly).

• One change-management workflow for new tools, connectors, and model versions.

• One incident playbook: prompt injection handling, data exfil alerts, and model output safety events.

Board-level pressures that force the enclave conversation

If your organization is increasing AI usage in Support, Sales, Finance, or Legal Ops in 2025 planning cycles, enclave design becomes a governance prerequisite, not a technical nice-to-have. The board question is simple: “Can we prove we control this?”

• Audit readiness: AI usage becomes part of SOC 2/ISO evidence expectations; screenshots don’t scale.

• Data residency and cross-border transfer risk: regulators and customers ask for region guarantees.

• Third-party and vendor risk: procurement questionnaires now ask about model providers, training, and retention.

• Operational risk: ungoverned pilots create “shadow AI” with unknown exposure and no kill switch.

Days 1–7: audit the workflows, not just the models

This phase is where we prevent wasted build time. You don’t want to harden an enclave for a workflow that should have been Tier 1 (non-sensitive), and you don’t want to pilot on sensitive data without your evidence plan signed off.

• Inventory candidate workflows (e.g., contract triage, support escalation summaries, policy Q&A).

• Classify data touched (PII/PHI/PCI, confidential commercial terms, regulated records).

• Define enclave tier per workflow (VPC, on‑prem, hybrid) and required regions.

• Agree evidence requirements with Audit (log fields, retention, access review cadence).

Days 8–20: stand up the enclave ‘paved road’

DeepSpeed AI typically uses orchestration and observability layers that fit your stack (CloudWatch/Azure Monitor/GCP Ops, Datadog, OpenTelemetry). Vector databases are deployed in-region (managed or self-hosted) depending on your residency constraints.

• Deploy the gateway (in VPC or on‑prem) with RBAC, redaction, prompt/response logging, and connector allowlists.

• Configure private networking: PrivateLink/peering, no public endpoints, egress deny-by-default.

• Set SLOs and guardrails: latency targets, refusal thresholds, confidence thresholds for auto-actions.

• Wire observability: traces, metrics, alerting; integrate with ServiceNow/Jira for incidents.

Days 21–30: pilot one sensitive workload and generate evidence automatically

The measurable win isn’t “we installed an LLM.” It’s: “We shipped a sensitive workflow inside a controlled enclave, with complete audit evidence, without blocking delivery teams.”

• Pick one high-value, high-risk workflow (e.g., contract clause extraction + risk summary).

• Run parallel with human review; measure cycle time, error rate, and policy compliance.

• Produce an “audit bundle”: log samples, access review output, policy-as-code, and retention proof.

• Hold a Security/Legal/Audit sign-off meeting with artifacts, not slides.

How to use this artifact

• Attach it to your DPIA/TRA packet and vendor/security review so approvals are repeatable.

• Give Engineering a clear contract: what’s allowed, what’s blocked, and what needs human approval.

• Use it as the basis for continuous controls monitoring (drift detection + evidence).

What changed operationally

Business outcome (the number a COO/CFO repeats): ~410 Legal Ops hours returned per quarter by reducing manual clause extraction and first-pass risk summarization—without relaxing confidentiality controls.

Stakeholders didn’t “trust AI.” They trusted the boundary: private connectivity, controlled egress, and audit logs that matched existing SOC 2 evidence patterns.

• Legal Ops stopped emailing redlines and clause questions back and forth; the enclave workflow produced a sourced summary with approved playbook language.

• Security got a single control surface (gateway + logs) instead of one-off tool exceptions.

• Audit got queryable evidence (who accessed what, when, and from which data source).

What you get in 30 days

If you’re trying to unblock sensitive AI use cases without creating audit debt, book a 30-minute assessment to scope the enclave tiering and the first workflow that’s worth hardening. We’ll map controls to your frameworks and deliver a sub-30-day pilot that Security can defend.

• Enclave reference architecture (VPC, on‑prem, or hybrid) aligned to your residency and control requirements.

• Gateway policy + evidence plan: RBAC, prompt logging, redaction, retention, and approval workflows.

• One production-grade pilot (e.g., Document and Contract Intelligence or an AI Knowledge Assistant) running inside the enclave with measurable cycle-time impact.

Do these to reduce approvals from months to days

Most organizations don’t need a 50-page AI policy to start. They need a defensible enclave standard that turns “it depends” into a repeatable control decision.

• Publish a 1-page enclave standard: tiers, regions, and “no-go” data types.

• Stand up a single LLM gateway with logging + redaction before approving any new copilot.

• Require every pilot to ship with an evidence bundle (logs + access review + retention proof).